{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the training dataset\n",
    "data_train=pd.read_csv('train.csv')\n",
    "data_test=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With Fare (0.25730652238496243, 6.120189341921873e-15)\n",
      "With Age (nan, 1.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ANACONDA\\lib\\site-packages\\scipy\\stats\\stats.py:5256: RuntimeWarning: invalid value encountered in less\n",
      "  x = np.where(x < 1.0, x, 1.0)  # if x > 1 then return 1.0\n"
     ]
    }
   ],
   "source": [
    "#Trying some data Visulaization Techniques\n",
    "from scipy.stats import pearsonr #Importing the Pearson Correlation Coefficient calc\n",
    "print(\"With Fare\",pearsonr(data_train[\"Survived\"].values,data_train[\"Fare\"].values ))\n",
    "print(\"With Age\",pearsonr(data_train[\"Survived\"].values,data_train[\"Age\"].values ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Q'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-55ab8a49b5f1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mbest_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mSelectKBest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscore_func\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchi2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mfit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbest_features\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[0mdfscores\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscores_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ANACONDA\\lib\\site-packages\\sklearn\\feature_selection\\univariate_selection.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    339\u001b[0m         \u001b[0mself\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m         \"\"\"\n\u001b[1;32m--> 341\u001b[1;33m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'csr'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'csc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore_func\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ANACONDA\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    745\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    746\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 747\u001b[1;33m                     estimator=estimator)\n\u001b[0m\u001b[0;32m    748\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    749\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[1;32mC:\\ANACONDA\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    560\u001b[0m         \u001b[1;31m# make sure we actually converted to numeric:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    561\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdtype_numeric\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"O\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 562\u001b[1;33m             \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    563\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_nd\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    564\u001b[0m             raise ValueError(\"Found array with dim %d. %s expected <= 2.\"\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Q'"
     ]
    }
   ],
   "source": [
    "#apply SelectKBest class to extract top  features\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "X=data_train.iloc[:,3:]\n",
    "Y=data_train.iloc[:,2:3]\n",
    "best_features=SelectKBest(score_func=chi2, k=3)\n",
    "fit=best_features.fit(X,Y)\n",
    "dfscores=pd.DataFrame(fit.scores_)\n",
    "df.columns=pd.DataFrame(X.columns)\n",
    "\n",
    "print(\"DFscores\",dfscores)\n",
    "print(\"DFcolumns\",dfcolumns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preprocessing train data\n",
    "data_train=data_train.drop(labels=[\"Name\",\"Ticket\",\"Cabin\",\"Embarked\"],axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Representing Sex with numbers\n",
    "gender = {'male': 0,'female': 1}#Gender dictionary\n",
    "data_train.Sex=[gender[item] for item in data_train.Sex]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using sklearn's LabelEncoder to perform one_hot_encoding in Pclass\n",
    "from sklearn import preprocessing\n",
    "onehot=preprocessing.LabelEncoder()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Trying one hot encoding using Pandas\n",
    "data_train1=pd.get_dummies(data_train[\"Pclass\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train=data_train.drop(labels=['Pclass'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train=data_train.join(data_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Survived\n",
      "0           0\n",
      "1           1\n",
      "2           1\n",
      "3           1\n",
      "4           0\n",
      "5           0\n",
      "6           0\n",
      "7           0\n",
      "8           1\n",
      "9           1\n",
      "10          1\n",
      "11          1\n",
      "12          0\n",
      "13          0\n",
      "14          0\n",
      "15          1\n",
      "16          0\n",
      "17          1\n",
      "18          0\n",
      "19          1\n",
      "20          0\n",
      "21          1\n",
      "22          1\n",
      "23          1\n",
      "24          0\n",
      "25          1\n",
      "26          0\n",
      "27          0\n",
      "28          1\n",
      "29          0\n",
      "..        ...\n",
      "861         0\n",
      "862         1\n",
      "863         0\n",
      "864         0\n",
      "865         1\n",
      "866         1\n",
      "867         0\n",
      "868         0\n",
      "869         1\n",
      "870         0\n",
      "871         1\n",
      "872         0\n",
      "873         0\n",
      "874         1\n",
      "875         1\n",
      "876         0\n",
      "877         0\n",
      "878         0\n",
      "879         1\n",
      "880         1\n",
      "881         0\n",
      "882         0\n",
      "883         0\n",
      "884         0\n",
      "885         0\n",
      "886         0\n",
      "887         1\n",
      "888         0\n",
      "889         1\n",
      "890         0\n",
      "\n",
      "[891 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data_train.iloc[:,1:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filling up Nan in age\n",
    "data_train['Age']=data_train['Age'].fillna(method='ffill',axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.36762635 0.05284785 0.06030511 0.39552006 0.03528445 0.00858176\n",
      " 0.07983443]\n",
      "Age      0.367626\n",
      "SibSp    0.052848\n",
      "Parch    0.060305\n",
      "Fare     0.395520\n",
      "1        0.035284\n",
      "2        0.008582\n",
      "3        0.079834\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ANACONDA\\lib\\site-packages\\sklearn\\ensemble\\forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ANACONDA\\lib\\site-packages\\ipykernel_launcher.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "#Trying out the Feature Importance Metric to see which features are the most important\n",
    "X=data_train.iloc[:,3:]\n",
    "y=data_train.iloc[:,1:2]\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "model=ExtraTreesClassifier()\n",
    "model.fit(X,y)\n",
    "print(model.feature_importances_)\n",
    "\n",
    "#Now plotting the graphs\n",
    "feat_importances=pd.Series(model.feature_importances_,index=X.columns)\n",
    "\n",
    "print(feat_importances)\n",
    "#feat_importances.nlargest(3).plot(kind='barh')\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 10)\n"
     ]
    }
   ],
   "source": [
    "print(data_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Trying to do feature selection with Correlation Matrix with HeatMap\n",
    "corrmat=data_train.corr()\n",
    "top_corr_features=corrmat.index\n",
    "plt.figure(figsize=(10,10))\n",
    "#plotting the heatmap\n",
    "g=sns.heatmap(data_train[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PassengerId  Survived  Sex   Age  SibSp  Parch     Fare  1  2  3\n",
      "0            1         0    0  22.0      1      0   7.2500  0  0  1\n",
      "1            2         1    1  38.0      1      0  71.2833  1  0  0\n",
      "2            3         1    1  26.0      0      0   7.9250  0  0  1\n",
      "3            4         1    1  35.0      1      0  53.1000  1  0  0\n",
      "4            5         0    0  35.0      0      0   8.0500  0  0  1\n",
      "5            6         0    0   0.0      0      0   8.4583  0  0  1\n",
      "6            7         0    0  54.0      0      0  51.8625  1  0  0\n",
      "7            8         0    0   2.0      3      1  21.0750  0  0  1\n",
      "8            9         1    1  27.0      0      2  11.1333  0  0  1\n",
      "9           10         1    1  14.0      1      0  30.0708  0  1  0\n"
     ]
    }
   ],
   "source": [
    "print(data_train.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['SibSp'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-eb7312fd7b75>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#After observing the HEATMAP dropping the features that have very less correlation with the Survived column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdata_train\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"SibSp\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mdata_train\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"PassengerId\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ANACONDA\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3695\u001b[0m                                            \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3696\u001b[0m                                            \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3697\u001b[1;33m                                            errors=errors)\n\u001b[0m\u001b[0;32m   3698\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3699\u001b[0m     @rewrite_axis_style_signature('mapper', [('copy', True),\n",
      "\u001b[1;32mC:\\ANACONDA\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3109\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3110\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3111\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3112\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ANACONDA\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   3141\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3142\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3143\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3144\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ANACONDA\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   4402\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'ignore'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4403\u001b[0m                 raise KeyError(\n\u001b[1;32m-> 4404\u001b[1;33m                     '{} not found in axis'.format(labels[mask]))\n\u001b[0m\u001b[0;32m   4405\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4406\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['SibSp'] not found in axis\""
     ]
    }
   ],
   "source": [
    "#After observing the HEATMAP dropping the features that have very less correlation with the Survived column\n",
    "\n",
    "data_train=data_train.drop(labels=[\"SibSp\"],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train=data_train.drop(labels=[\"PassengerId\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Survived  Sex   Age  Parch     Fare  1  2  3\n",
      "0         0    0  22.0      0   7.2500  0  0  1\n",
      "1         1    1  38.0      0  71.2833  1  0  0\n",
      "2         1    1  26.0      0   7.9250  0  0  1\n",
      "3         1    1  35.0      0  53.1000  1  0  0\n"
     ]
    }
   ],
   "source": [
    "print(data_train.head(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train_features=data_train.iloc[:,2:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[35.    0.    8.05  0.    0.    1.  ]\n"
     ]
    }
   ],
   "source": [
    "print(Train_features[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train_label=data_train.iloc[:,1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [0]]\n"
     ]
    }
   ],
   "source": [
    "print(Train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ANACONDA\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:363: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#One Hot Encoding the labels\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "oneHot=OneHotEncoder()\n",
    "oneHot.fit(Train_label) \n",
    "Train_Label = oneHot.transform(Train_label).toarray() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " ...\n",
      " [0. 1.]\n",
      " [1. 0.]\n",
      " [1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(Train_Label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(Train_features,Train_Label,test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 596 2\n"
     ]
    }
   ],
   "source": [
    "#Now defining the model\n",
    "M=X_train.shape[1]\n",
    "N=X_train.shape[0]\n",
    "\n",
    "LC=Y_train.shape[1]\n",
    "print(M,N,LC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 1.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " ...\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining the inputs of the Neural Network\n",
    "\n",
    "X=tf.placeholder(tf.float32,[None,M])\n",
    "Y=tf.placeholder(tf.float32,[None,LC])\n",
    "epochs=3000\n",
    "learning_rate=0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-53-ede1404164aa>:6: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See @{tf.nn.softmax_cross_entropy_with_logits_v2}.\n",
      "\n",
      "WARNING:tensorflow:From C:\\ANACONDA\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py:118: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n"
     ]
    }
   ],
   "source": [
    "#Defining the model \n",
    "initializer=tf.contrib.layers.xavier_initializer()#Initializing the weight matrix\n",
    "h0=tf.layers.dense(X,units=5,activation=tf.nn.relu,kernel_initializer=initializer)\n",
    "h1=tf.layers.dense(h0,units=LC,activation=tf.nn.sigmoid)\n",
    "\n",
    "cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=h1))\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n",
    "\n",
    "predicted=tf.nn.sigmoid(h1)\n",
    "correct_pred=tf.equal(tf.round(predicted),Y)\n",
    "accuracy=tf.reduce_mean(tf.cast(correct_pred,tf.float32))\n",
    "\n",
    "cost_history = np.empty(shape=[1],dtype=float)\n",
    "\n",
    "init = tf.initialize_all_variables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:     0\tLoss: 0.615\tAcc: 51.01%\n",
      "Step:   100\tLoss: 0.593\tAcc: 50.92%\n",
      "Step:   200\tLoss: 0.589\tAcc: 52.01%\n",
      "Step:   300\tLoss: 0.586\tAcc: 53.78%\n",
      "Step:   400\tLoss: 0.584\tAcc: 56.21%\n",
      "Step:   500\tLoss: 0.582\tAcc: 59.90%\n",
      "Step:   600\tLoss: 0.581\tAcc: 62.58%\n",
      "Step:   700\tLoss: 0.581\tAcc: 64.26%\n",
      "Step:   800\tLoss: 0.580\tAcc: 65.27%\n",
      "Step:   900\tLoss: 0.580\tAcc: 65.60%\n",
      "Step:  1000\tLoss: 0.579\tAcc: 65.94%\n",
      "Step:  1100\tLoss: 0.579\tAcc: 66.36%\n",
      "Step:  1200\tLoss: 0.579\tAcc: 66.28%\n",
      "Step:  1300\tLoss: 0.579\tAcc: 66.78%\n",
      "Step:  1400\tLoss: 0.578\tAcc: 66.78%\n",
      "Step:  1500\tLoss: 0.578\tAcc: 66.86%\n",
      "Step:  1600\tLoss: 0.578\tAcc: 66.69%\n",
      "Step:  1700\tLoss: 0.578\tAcc: 66.86%\n",
      "Step:  1800\tLoss: 0.578\tAcc: 67.03%\n",
      "Step:  1900\tLoss: 0.577\tAcc: 67.11%\n",
      "Step:  2000\tLoss: 0.577\tAcc: 67.45%\n",
      "Step:  2100\tLoss: 0.577\tAcc: 67.53%\n",
      "Step:  2200\tLoss: 0.577\tAcc: 67.53%\n",
      "Step:  2300\tLoss: 0.577\tAcc: 67.53%\n",
      "Step:  2400\tLoss: 0.577\tAcc: 67.53%\n",
      "Step:  2500\tLoss: 0.577\tAcc: 67.62%\n",
      "Step:  2600\tLoss: 0.577\tAcc: 67.53%\n",
      "Step:  2700\tLoss: 0.576\tAcc: 67.62%\n",
      "Step:  2800\tLoss: 0.576\tAcc: 67.62%\n",
      "Step:  2900\tLoss: 0.576\tAcc: 67.62%\n",
      "Step:  3000\tLoss: 0.576\tAcc: 67.70%\n",
      "Step:  3100\tLoss: 0.576\tAcc: 67.79%\n",
      "Step:  3200\tLoss: 0.576\tAcc: 67.79%\n",
      "Step:  3300\tLoss: 0.576\tAcc: 68.04%\n",
      "Step:  3400\tLoss: 0.576\tAcc: 68.04%\n",
      "Step:  3500\tLoss: 0.576\tAcc: 68.04%\n",
      "Step:  3600\tLoss: 0.576\tAcc: 68.12%\n",
      "Step:  3700\tLoss: 0.576\tAcc: 68.12%\n",
      "Step:  3800\tLoss: 0.576\tAcc: 68.12%\n",
      "Step:  3900\tLoss: 0.576\tAcc: 68.04%\n",
      "Step:  4000\tLoss: 0.576\tAcc: 68.12%\n",
      "Step:  4100\tLoss: 0.575\tAcc: 68.04%\n",
      "Step:  4200\tLoss: 0.576\tAcc: 68.04%\n",
      "Step:  4300\tLoss: 0.575\tAcc: 68.04%\n",
      "Step:  4400\tLoss: 0.575\tAcc: 68.04%\n",
      "Step:  4500\tLoss: 0.575\tAcc: 68.12%\n",
      "Step:  4600\tLoss: 0.575\tAcc: 68.12%\n",
      "Step:  4700\tLoss: 0.575\tAcc: 68.12%\n",
      "Step:  4800\tLoss: 0.575\tAcc: 68.37%\n",
      "Step:  4900\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  5000\tLoss: 0.575\tAcc: 68.46%\n",
      "Step:  5100\tLoss: 0.575\tAcc: 68.37%\n",
      "Step:  5200\tLoss: 0.575\tAcc: 68.37%\n",
      "Step:  5300\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  5400\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  5500\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  5600\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  5700\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  5800\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  5900\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  6000\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  6100\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  6200\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  6300\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  6400\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  6500\tLoss: 0.574\tAcc: 68.54%\n",
      "Step:  6600\tLoss: 0.574\tAcc: 68.54%\n",
      "Step:  6700\tLoss: 0.574\tAcc: 68.54%\n",
      "Step:  6800\tLoss: 0.574\tAcc: 68.54%\n",
      "Step:  6900\tLoss: 0.574\tAcc: 68.54%\n",
      "Step:  7000\tLoss: 0.575\tAcc: 68.54%\n",
      "Step:  7100\tLoss: 0.574\tAcc: 68.54%\n",
      "Step:  7200\tLoss: 0.574\tAcc: 68.54%\n",
      "Step:  7300\tLoss: 0.574\tAcc: 68.54%\n",
      "Step:  7400\tLoss: 0.574\tAcc: 68.62%\n",
      "Step:  7500\tLoss: 0.574\tAcc: 68.62%\n",
      "Step:  7600\tLoss: 0.574\tAcc: 68.62%\n",
      "Step:  7700\tLoss: 0.575\tAcc: 68.62%\n",
      "Step:  7800\tLoss: 0.574\tAcc: 68.62%\n",
      "Step:  7900\tLoss: 0.574\tAcc: 68.62%\n",
      "Step:  8000\tLoss: 0.574\tAcc: 68.62%\n",
      "Step:  8100\tLoss: 0.574\tAcc: 68.62%\n",
      "Step:  8200\tLoss: 0.574\tAcc: 68.54%\n",
      "Step:  8300\tLoss: 0.574\tAcc: 68.54%\n",
      "Step:  8400\tLoss: 0.574\tAcc: 68.62%\n",
      "Step:  8500\tLoss: 0.574\tAcc: 68.62%\n",
      "Step:  8600\tLoss: 0.575\tAcc: 68.71%\n",
      "Step:  8700\tLoss: 0.574\tAcc: 68.62%\n",
      "Step:  8800\tLoss: 0.574\tAcc: 68.71%\n",
      "Step:  8900\tLoss: 0.574\tAcc: 68.71%\n",
      "Step:  9000\tLoss: 0.574\tAcc: 68.62%\n",
      "Step:  9100\tLoss: 0.574\tAcc: 68.62%\n",
      "Step:  9200\tLoss: 0.574\tAcc: 68.71%\n",
      "Step:  9300\tLoss: 0.574\tAcc: 68.62%\n",
      "Step:  9400\tLoss: 0.574\tAcc: 68.71%\n",
      "Step:  9500\tLoss: 0.574\tAcc: 68.71%\n",
      "Step:  9600\tLoss: 0.574\tAcc: 68.54%\n",
      "Step:  9700\tLoss: 0.574\tAcc: 68.71%\n",
      "Step:  9800\tLoss: 0.574\tAcc: 68.71%\n",
      "Step:  9900\tLoss: 0.575\tAcc: 68.46%\n"
     ]
    }
   ],
   "source": [
    "#Creating the session\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for epoch in range (10000):\n",
    "        loss,_,acc=sess.run([cost ,optimizer , accuracy], feed_dict={X:X_train, Y:Y_train})\n",
    "        cost_history=np.append(cost_history,acc)\n",
    "        if epoch%100==0:\n",
    "            print(\"Step: {:5}\\tLoss: {:.3f}\\tAcc: {:.2%}\".format(epoch, loss, acc))\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
